A digital image is a two-dimensional array of pixel elements. Examples of two-dimensional pixel arrays for digital images are 768.times.512 pixels or 1024.times.1024 pixels. Each pixel of a digital image may be represented by digital values which provide the intensity and color of the image. A digital image may be composed of a single color channel (gray scale) or multiple color channels, such as RGB or YCC, where each pixel in the digital image has a value for each color channel of an image. Generally, these digital images are called continuous tone digital images.
Often, a digital image will have pixel regions with noisy or corrupted pixel values due to defects in the original digitized image or defects introduced into the image by the digitizing process. Pixels within such regions are referred to as defect pixels and pixels outside those regions are referred to as non-defect pixels.
Prior automatic image editing systems have had limited success in reconstructing these defect pixel regions. Typically, these systems attempt to reconstruct the regions by estimating values for each defect pixel using values from the closest non-defect pixel or by averaging the values from a number of the closest non-defect pixels. Although this reconstruction is adequate in small compact defect regions of only a few pixels in size, it fails to provide proper reconstruction of larger (e.g., long and narrow) defect regions. This results in reconstructed defect regions which are often visually inconsistent with the non-defect pixels in the image. One reason for this inconsistency is that the selection of non-defect pixels used in estimating values of defect pixels may be improper, which causes blurring of the reconstructed image in the defect region. Another reason for this inconsistency may be the result of improper reconstruction of edges of objects in the image which intersect the defect regions. This results in reconstructed edges appearing bent or discontinuous.